A race condition is defined as a phenomenon during which the output of a system is indeterminate and is dependent on the scheduling algorithm and the order in which tasks are scheduled and executed. Critical sections indicate shared resources that are accessed by multiple processes or threads in a concurrent application, which can lead to unexpected, and even erroneous, behavior. A race condition occurs when two or more threads/processes access and alter a shared resource simultaneously, resulting in mishandled and corrupted data. Race conditions also have significant implications in real-life applications, such as security, operating systems, and networking.

Since the race conditions that we observed arose when multiple threads or processes accessed and wrote to a shared resource simultaneously, the key idea for solving race conditions is to isolate the execution of different threads/processes, especially when interacting with a shared resource. With locks, we can turn a shared resource in a concurrent program into a critical section, whose integrity of data is guaranteed to be protected. However, there are a number of disadvantages to using locks: with enough locks implemented in a concurrent program, the whole program might become sequential; locks don't lock anything.

In the next chapter, we will consider one of the biggest problems in Python concurrent programming: the infamous Global Interpreter Lock (GIL). You will learn about the basic idea behind the GIL, its purposes, and how to effectively work with it in concurrent Python applications.